Web Analytics is often used by marketing staff members to determine their effectiveness. This is a real-life example of how I recently helped a company measure the effectiveness of their internally driven e-mail campaign.

A company I work provides training to their clients on the products they have purchased, as part of their on-going support.

Before I started working with this company, only basic analytics from the website was tracked. Not even all of the pages were being actively tracked. So I have been slowly working on adding a complete view of the website usage based upon analytics.

It recently started sending out targeted e-mail newsletters showcasing their latest course offerings based upon what classes the user has previously attended. However, the first e-mails required that the user manually enter the web address, and search for the class. While enrollments improved, it wasn’t as much as had been hopped.

To make things easier, I developed a method to allow the user to directly access the class. Given that a software vendor was used for the site, which has limited customization abilities, this was not as simple as one would think. However, with my web development background it was possible. By using a special link and modifying our analytics package, we were able to start tracking how many people followed the link from the e-mails that went out.

While it was always intuitive that they received more enrollments based upon the e-mails, they had no way to determine how many people were accessed the site based upon the e-mails. They could see that on a given day, enrollments went up - but how about the following days.

Often e-mail isn’t opened or acted upon in the first day due to people being out the office, having to get approval for the expenditure, etc. We can now prove this by seeing web hits from our clients 3 or 4 days after the initial e-mail goes out.

By tracking the information a little more closely, we can see the return on investment (ROI) of the internal e-mailing system and validate its continued use. This has the staff excited about continuing to use this new marketing system, as well as bringing in additional clients for the training.

It’s one thing to look at your reports, its another to start to draw conclusions from your data. Different people will look at the data in different ways. Most often, the reports are looked at by the marketing department/person to see how to better market the site, what Search Engine optimization needs to occur, and measure the effectiveness of their pay-per-click ads. All of these are important uses of the web analytics, but they are not the only uses.

I want to look at a specific example of how I, as a web developer, recently went about making a site faster based upon the results of some analytics reports.

The 80/20 rule of the web

We’ve all heard of the 80/20 rule. On the web it would 80% of people only use 20% of your web site. In reality however, depending upon your site, it could be more like 90/10. A novice web developer will try to optimize all of the pages. An experienced web developer doesn’t bother with those pages that receive only the smallest amount of visitors.

On an Intranet web site I maintain, which tracks the employees’ internal education process, I was looking at the internal search feature. The search results are loaded into the same page. When you count the results from the searches, this page accounted for 24% of all of the page views, making it the second most visited page on the site. (The #1 page is mostly static text, so no real improvements could come from there.)

This means for every 2 people to visit the site, approximately 3 searches were performed in addition to the normal page load. So almost 1 out of every 4 page views was to this internal search engine, and some people were searching multiple times.

This became an area to consider improving. As a little improvement here, would have more effect than a large improvement on a page that is only viewed 1% or less of the time.

Too combat this, I decided to load the results in with AJAX instead of reloading the whole page. This process would improve search response in 3 ways.

Network Connections

Each time a file is downloaded over the Internet, the web browser checks to see if there is a newer version on the server, or if it can use a local file it has already downloaded (cached). Each time it checks to use a local version or downloads a new copy, the server is using up resources, and your end-user has to wait for the files to finish checking/downloading. Because the number of network connections has been reduced, other people can be accessing the site at a faster rate, and the end-user appears to be faster as they don’t have to have their computer do as much work.

The search page referenced 15 external files, plus itself. Of those 12 wouldn’t have to be called anymore. So I was able to reduce the number network connections by 80% for the search results. This was our biggest savings. The number of network connections on a web server is limited by both number and bandwidth speed, therefore the more connections, the slower each connection appears.

Processing of Files

Knowing in advance that it was going to be a popular page, I had tried to make the page as efficient as possible initially. However, with some of the features, like remembering the previous search criteria, because you never left the page, no longer needed, I was able to simplify the page.

The initial page load took about the same amount of time, however when the search results displayed, it appeared to be less than 1 hundredth of a second faster to process the file, about 4.45% faster. While individually it does not yield much savings, when applied as many times as it is with the popularity of the page, the savings adds up, especially during peak times.

Size of Results File Download

I knew I would save a lot in this area. The search interface, consisting of 6 controls, and a variety of other HTML sections, would no longer have to be duplicated in loading the results. This would mean faster downloads, and improved “perception” of the speed of the web page/server. Based on the same sample set of data, there was an 11K reduction in the overhead of the search results, or approximately 14% smaller download. This was for a search with over 100 results. However, many times fewer results (20-50) are returned. So the savings in bandwidth, while still approximately 11K, would be a much higher percentage (30-75%).

Final Thoughts

Because I could determine from our analytics package which files were being processed more, I was able to get the most bang for my buck. This update will take some load off the server, and allow all the pages to benefit from this enhancement. Likewise, almost every visitor will notice a performance improvement in the site because of how often this page is viewed.

This is how a web developer can effectively use web analytics to improve the performance of the web site.

Learning how our web site works is an important task. We’ve seen some simple examples in previous steps (visits, page views, and unique visitors) as well as determining how long someone visited your site, and which was the last page they viewed. We’ve shown how some pages are viewed more than others, and where people exit your web site. We’ve even shown what pages people come to your web site.

What we will look at now is how did they get to your web site. Before they were on your web site, odds are they were somewhere else. Knowing this tidbit of information changes everything, and starts to allow us bring things together.

In the past articles I’ve had a decidedly split view, why this matters, why this doesn’t matter. This is going to be one sided - this matters. And the follow up where your (quality) visitors come from, will matter even more as we show you how the metrics from the previous articles start to fold in with where your visitors come from.

You will get visitors from all over the Internet. Web sites you don’t know about link to you, and such. However, all of these methods/places can be grouped into the following categories.

• Search Engine (organic) - this is where someone types in a question, and you site was on the list of answers. Depending upon the phrase entered, you may have little (rarely) or lots of competition.
• Search Engine (paid inclusion/pay-per-click) - You have purchased key words/phrases, and when someone enters a phrase, your ad comes up. That person clicks on the ad, instead of an organic result, you get charged a fee, and they go to your site.
• Online Ad Network - other networks beside those run from search engines exist, and you have purchased ads to be placed on websites based upon some criteria, and users came to your site from these ads.
• Another Website - another website, be it a standard site, a blog, a forum, etc. has your.
• Direct Access/Bookmark - someone typed in your web address or had previously bookmarked your site. This number is often a higher than it should be as sometimes the information used to pass where the visitor came from, isn’t passed. In that case, the Direct Access gets artificially inflated.

The question of course is, why is it this that matters? Here are how we look using the previous metrics to start to determine relevancy, and in how this metric matters.

Lets say you have a small business. And you have a few friends who like to refer their friends to your business. We all know that all referrals are not equal, and someone is going to send you better referrals than others. But how do you know which is which.

Obviously, the one that leads to the most sales is sending you the best referrals. But can you find out why that friend gives you the best referrals? Or better yet why?

You see, search engines, other sites that link to you, and your banner ads are your “friends”. They all want to send you visitors, just some are better at it than others. And just because someone refers a lot of people, doesn’t mean they are sending you the right visitors.

Lets say we look at some on-line ads you are using. You know that you are getting leads from them, sometimes 1000 a day. However, the users leave quickly after viewing the page. If you can segment your viewers and determine that 80% of the visitors that come to your site from this source “bounce” then you know you have a problem you have to address. You don’t know exactly why, maybe the leads/visitors aren’t qualified, or maybe the landing page (the page the visitor sees when clicking on the ad) is ineffective, but now you have something to start looking at to determine what to fix.

On the other hand, you have 1000 people coming from another web site, maybe Google Ad-Words and Yahoo’s pay-per-click. You can see from there that you have a bounce rate of 40%, and they tend to stay for 3 or 4 pages, by then 60-80% of people have left your site. Now we can look at a different set of problems. Could they not find how to buy? Did they get “fatigue” where they got tried of going through the pages? Now we can look at the exit pages, do they move from page to page as expected, or do they bounce around unexpectedly?

As you step through these numbers, I’m sure you will see that not all sources are equal. Some might result in lots of page views, but few conversions (sales, or requests for more information). This is why visitors, page views, and time on site are not necessarily good metrics. You have to learn what type of lead from a source is your best type.

Additionally you can start to segment your search engine data, be it from normal, organic, searches, or from paid search results. Now you can start to see what words and phrases people are using to find you, and how effective you are at converting those people into buyers. This is where your metric data starts to become real important.

Knowing what keywords and key phrases people are using to find your site when dealing with search engines. You might find that the phrases you would use, and you have optimized your site for, are not being used. Is this because there is too much competition, or because consumers use different words than expert/owners. It can also reveal when a phrase is ineffective, because it yields too low a conversion rate.

Knowing how many people repeat visit your site, and/or bookmark you site and directly access it indicates how valuable other’s feel your web site is. Likewise, if you find that most people visit from a given source once, but never return, you can start to see that they don’t place a high value on your site.

You may not be able to determine why people are leaving your site, but you can start to develop some good theories. Just start looking at it from a users point of view, and see what if anything you have to do to enhance your site to increase those conversions.

Learning how our web site works is an important task. We’ve seen some simple examples in previous steps (visits, page views, and unique visitors) as well as determining how long someone visited your site, and which was the last page they viewed.Most of these metrics were simple to user, and that is fine. However, now we want to look at the flip-side of which pages people leave on, that is what pages they are viewing, and which ones do they view first. This is which pages people go to first, and how many times was a page shown to someone. Let’s look at the larger picture: which pages were viewed, then look to see what order they saw the pages.

Pages Viewed may go by different names, such as Top Content, or Most Viewed Pages, tells you which pages are viewed most often. Depending upon your Analytics package, you may get this by file name, by web page title, or both. Depending upon your needs that you will look at. When they use the file name, anything after the file, which might change, will be displayed as a separate entry, despite the physical file being the same. This is because the part after the file name might change what the file displays. Consider these two examples:

• Product.php?id=21 - this might display product information for Widgets
• Product.php?id=32 - might display product information for Gizmos.

Because the content can change, each entry is listed separately. Pages based on internal search forms fall into this category quite often, and can cause quite a bit of confusion. Even if the parameters are reversed, it will often view the two entries as two separate pages. This is intentional in case someone wants to track the path on how they got to the file, or other similar things.

Why this is important: from here were can start to see what files are viewed the most often. Clients are often surprised to find out that it is not necessarily the home page (more on that in a bit). They are also (sometimes) surprised that some pages are viewed a lot more than others. Consider the following graphic. At the top, you will notice over 1,700 URLs (web page addresses), in the bottom graph, you will notice there are only about 30 page titles. This is for the same site, same time frame, the difference is the first graphic is for the page file name, and the second is the page titles. Because the file names are similar, but a lot of extra stuff is appended because of the search pages, the 1700 files are exaggerated. You have to find the report that works for you.

These reports came from Google Analytics, and they provide some additional information. We’ll look at that in the next article or two.

Why this doesn’t matter: outside of wanting to know what page is being displayed, this isn’t important. That is an important thing to understand. This report can’t show you why they went the page they viewed, if they found the information they were looking for, or, when viewed by itself, what did they do after viewing the page.

A count of individual page views is also unimportant because it doesn’t specify why the person has come to that page, possibly repeatedly. Are they lost? Do they have to go through a series of “hoops” to get where they want to go? Page views are a lazy man’s metric because it can look impressive, but not provide any key insight.

Top Landing Page, sometimes called an Entrance Page, is the page that the user enters the web site on. Many people naturally assume that someone will go to the home page first. They will spend lots of money making a cool splash screen, and not spend those resources on making the other pages better. There are many times another page is the used to enter the site. For example:

• Search Engines take people to the most relevant page, not the home page.
• Other people might link from their web page to a favorite article, product, or review.
• People bookmark the page in a site that helps them, not the homepage necessarily. (I commonly bookmark login pages - especially if I have to pay a bill on a web site.)
• Someone might send/receive an e-mail with a link to your site, that isn’t to the home page.

All of these and more cause other pages to be the “landing page”.

Why this is important: Knowing what pages people enter your site from, helps you know what pages to focus on. You should also watch to see if a change in your site/page increases or decreases the people entering your site through that page. While this wouldn’t necessarily define the cause and effect, it allows you to see some of what is happening and start to make an educated guess. It is also important to know if you will be moving a page, as it lets you know that other’s will be effected. Often if someone reaches your site, and it gives them an error page (404 is the Not Found Error) - they will leave your site and find the answer somewhere else. Knowing this can provide you with the information to take to your technical people to ensure that they use the proper redirects to make sure people don’t get lost on your site. (Hint: You should always redirect your pages…)

Why this doesn’t matter: When we put together this metric with some others, you will see how this can be a powerful metric. However, by itself, as with most other metrics, you will not be able to gather much information from it.